Kforce Inc
Base pay range
$100.00/hr - $120.00/hr Responsibilities
Designing, building, and optimizing all aspects of large-scale training and fine-tuning, from dataloading to inference, to maximize Model Flop Utilization (MFU) on large compute clusters Working closely and proactively with research scientists to translate models and algorithms into high-performance, production-ready code, integrating and testing the latest advancements Relentlessly profiling and resolving training performance bottlenecks, optimizing the entire training stack for speed and efficiency Contributing to the technology evaluations and selection of hardware, software, and cloud services for the AI infrastructure platform Using MLOps frameworks (MLFlow, WnB, etc.) to ensure best practices across the model lifecycle, ensuring reproducibility, reliability, and continuous improvement Creating thorough documentation for infrastructure and training procedures, staying updated on advancements in training strategies, and driving improvements in workflows and infrastructure Requirements
Master's degree or higher in Computer Science, Engineering, or a related technical field; candidates with more experience can be considered for a higher level or vice versa 5 or more years in a Data & AI (Artificial Intelligence) Engineer or Machine Learning Engineer, focusing on building and optimizing infrastructure for large-scale machine learning systems Deep practical expertise with AI frameworks (PyTorch, Jax, Pytorch Lightning, etc.), large-scale multi-node GPU training, and optimization strategies for large foundation models on distributed compute infrastructure Excellent problem‑solving, debugging, and performance optimization skills, with a data‑driven approach to identifying and resolving technical challenges Strong communication and teamwork skills, experience with MLOps best practices for model tracking, evaluation, and deployment Preferred Qualifications
Public GitHub profile with a track record of open‑source contributions to data engineering or deep learning infrastructure projects Experience writing CUDA/Triton/CUTLASS kernels Proficiency with performance monitoring and profiling tools for distributed training and data pipelines This role requires deep hands‑on expertise in distributed training, data engineering, MLOps, a proven track record of building scalable AI infrastructure. Our successful candidate will be a high‑agency individual demonstrating initiative, problem‑solving, and a commitment to delivering robust and scalable solutions for rapid prototyping and turnaround. Benefits
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave. Compensation Disclosure
The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors such as relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future. Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion until paid and may be modified in its discretion consistent with the law. This job is not eligible for bonuses, incentives or commissions. Employment Information
Seniority level: Associate Employment type: Contract Job function: Engineering and Information Technology Industries: Oil and Gas, Utilities, and Renewable Energy Semiconductor Manufacturing EEO Statement
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status. Referrals increase your chances of interviewing at Kforce Inc by 2x.
#J-18808-Ljbffr
$100.00/hr - $120.00/hr Responsibilities
Designing, building, and optimizing all aspects of large-scale training and fine-tuning, from dataloading to inference, to maximize Model Flop Utilization (MFU) on large compute clusters Working closely and proactively with research scientists to translate models and algorithms into high-performance, production-ready code, integrating and testing the latest advancements Relentlessly profiling and resolving training performance bottlenecks, optimizing the entire training stack for speed and efficiency Contributing to the technology evaluations and selection of hardware, software, and cloud services for the AI infrastructure platform Using MLOps frameworks (MLFlow, WnB, etc.) to ensure best practices across the model lifecycle, ensuring reproducibility, reliability, and continuous improvement Creating thorough documentation for infrastructure and training procedures, staying updated on advancements in training strategies, and driving improvements in workflows and infrastructure Requirements
Master's degree or higher in Computer Science, Engineering, or a related technical field; candidates with more experience can be considered for a higher level or vice versa 5 or more years in a Data & AI (Artificial Intelligence) Engineer or Machine Learning Engineer, focusing on building and optimizing infrastructure for large-scale machine learning systems Deep practical expertise with AI frameworks (PyTorch, Jax, Pytorch Lightning, etc.), large-scale multi-node GPU training, and optimization strategies for large foundation models on distributed compute infrastructure Excellent problem‑solving, debugging, and performance optimization skills, with a data‑driven approach to identifying and resolving technical challenges Strong communication and teamwork skills, experience with MLOps best practices for model tracking, evaluation, and deployment Preferred Qualifications
Public GitHub profile with a track record of open‑source contributions to data engineering or deep learning infrastructure projects Experience writing CUDA/Triton/CUTLASS kernels Proficiency with performance monitoring and profiling tools for distributed training and data pipelines This role requires deep hands‑on expertise in distributed training, data engineering, MLOps, a proven track record of building scalable AI infrastructure. Our successful candidate will be a high‑agency individual demonstrating initiative, problem‑solving, and a commitment to delivering robust and scalable solutions for rapid prototyping and turnaround. Benefits
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave. Compensation Disclosure
The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors such as relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future. Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion until paid and may be modified in its discretion consistent with the law. This job is not eligible for bonuses, incentives or commissions. Employment Information
Seniority level: Associate Employment type: Contract Job function: Engineering and Information Technology Industries: Oil and Gas, Utilities, and Renewable Energy Semiconductor Manufacturing EEO Statement
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status. Referrals increase your chances of interviewing at Kforce Inc by 2x.
#J-18808-Ljbffr